My Corpus
In this project, I analyze my tracks ‘desmond-l-1’ and ‘desmond-l-2’ compared to the Computational Musicology 2025 corpus using Essentia features such as danceability, energy, valence, and instrumentalness.
I curated my tracks using generative AI tool Stable Audio. The prompts I used were based on two of my favorite artists: KI/KI and Marlon Hoffstadt. Both DJs are known for their high energetic music, combining acid and trance with techno.
The table below presents the extracted Essentia feature values for my generated tracks.| filename | approachability | arousal | danceability | engagingness | instrumentalness | tempo | valence |
|---|---|---|---|---|---|---|---|
| desmond-l-1 | 0.4008293 | 5.525241 | 0.6376823 | 0.8862919 | 0.7578623 | 93 | 5.837563 |
| desmond-l-2 | 0.3221814 | 6.353199 | 0.9561019 | 1.0029999 | 0.5750142 | 100 | 5.886079 |
The relationship between musical positiveness and emotion
This graph explores the relationship between arousal and valence, with the danceability represented by the color.
My tracks, desmond-l-1, and desmond-l-2 score high in arousal and valence, meaning they are energetic and also emotionally positive. This aligns perfect with the sound of KI/KI and Marlon Hoffstadt.
How rave-ready are my tracks compared to the Class Corpus?
Compared to the class corpus, my tracks exhibit a higher combination of energy, emotional positivity, and danceability. This suggests that they are:
- More suited for raves than the average track in the class corpus.
- More danceable than most class tracks, making them well-suited for a high-energy electronic music setting.